Mobile robots are becoming increasingly commonplace and are used in such diverse fields as space exploration, lawn mowing and floor cleaning. Recently there has been a rapid advancement in the field of robotic floor cleaning devices, especially vacuum cleaners, the primary objective of which is to navigate a user's home autonomously and unobtrusively whilst cleaning the floor.
In performing this task, a robotic vacuum cleaner has to navigate the area which it is required to clean. Some robots are provided with a rudimentary navigation system whereby the robot uses what is sometimes referred to as a “random bounce” method whereby the robot will travel in any given direction until it meets an obstacle, at which time the robot will turn and travel in another random direction until another obstacle is met. Over time, it is hoped that the robot will have covered as much of the floor space requiring to be cleaned as possible. Unfortunately, these random bounce navigation schemes have been found to be lacking, and often large areas of the floor that should be cleaned will be completely missed.
Accordingly, better navigation methods are being researched and adopted in mobile robots. For example, Simultaneous Localisation and Mapping (SLAM) techniques are now starting to be adopted in some robots. These SLAM techniques allow a robot to adopt a more systematic navigation pattern by viewing, understanding, and recognising the area around it. Using SLAM techniques, a more systematic navigation pattern can be achieved, and as a result, in the case of a robotic vacuum cleaner, the robot will be able to more efficiently clean the required area.
Robots that use SLAM techniques need a vision system that is capable of capturing still or moving images of the surrounding area. High contrast features (sometimes referred to as landmark features) within the images such as the corner of a table or the edge of a picture frame are then used by the SLAM system to help the robot build up a map of the area, and determine its location within that map using triangulation. In addition, the robot can use relative movement of features that it detects within the images to analyse its speed and movement.
SLAM techniques are extremely powerful, and allow for a much improved navigation system. However, the SLAM system can only function correctly provided it is able to detect enough features within the images captured by the vision system. As such, it has been found that some robots struggle to successfully navigate in rooms that have low-light conditions or where the images captured by the vision system suffer from poor contrast. Some robots are therefore restricted to navigating during the day when there is sufficient ambient light available. In the case of a robotic floor cleaner, this may not be desirable because a user may wish to schedule their robot floor cleaner to clean at night while they are sleeping. To overcome this problem, some robots have been provided with a light which acts as a headlight that can be turned on and off as required to improve images captured by a camera, and assist the robot to see in the direction in which it is travelling. An example of this is described in US 2013/0056032.
However, there are problems associated with using headlights on robots. In order that autonomous robots can navigate freely around an area that may contain obstacles such as furniture, they are typically provided with an on-board power source in the form of a battery. The use of headlights can decrease the battery life of the robot, which means that the robot will be forced to return to a charging station within a smaller amount of time. This in turn means that the robot will only be able to clean a smaller area between charges than it would have otherwise been able to if it did not have to use headlights to navigate.